Abstract
Deep learning models stand for a new learning paradigm in artificial intelligence (AI) and machine learning. Recent breakthrough results in image analysis and speech recognition have generated a massive interest in this field because also applications in many other domains providing big data seem possible. On a downside, the mathematical and computational methodology underlying deep learning models is very challenging, especially for interdisciplinary scientists. For this reason, we present in this paper an introductory review of deep learning approaches including Deep Feedforward Neural Networks (D-FFNN), Convolutional Neural Networks (CNNs), Deep Belief Networks (DBNs), Autoencoders (AEs), and Long Short-Term Memory (LSTM) networks. These models form the major core architectures of deep learning models currently used and should belong in any data scientist's toolbox. Importantly, those core architectural building blocks can be composed flexibly—in an almost Lego-like manner—to build new application-specific network architectures. Hence, a basic understanding of these network architectures is important to be prepared for future developments in AI.
Highlights
We are living in the big data era where all areas of science and industry generate massive amounts of data
The topics we selected are focused on the core methodology of deep learning approaches including Deep Feedforward Neural Networks (DFFNN), Convolutional Neural Networks (CNNs), Deep Belief Networks (DBNs), Autoencoders (AEs), and Long Short-Term Memory (LSTM) networks
That difference is that a finite impulse recurrent networks (FRNs) is given by a directed acyclic graph (DAG) that can be unrolled in time and replaced with a Feedforward Neural Network, whereas an infinite impulse recurrent networks (IIRNs) is a directed cyclic graph (DCG) for which such an unrolling is not possible
Summary
We are living in the big data era where all areas of science and industry generate massive amounts of data This confronts us with unprecedented challenges regarding their analysis and interpretation. For this reason, there is an urgent need for novel machine learning and artificial intelligence methods that can help in utilizing these data. Due to the heterogeneity of deep learning approaches a comprehensive discussion is very challenging, and for this reason, previous reviews aimed at dedicated sub-topics. The topics we selected are focused on the core methodology of deep learning approaches including Deep Feedforward Neural Networks (DFFNN), Convolutional Neural Networks (CNNs), Deep Belief Networks (DBNs), Autoencoders (AEs), and Long Short-Term Memory (LSTM) networks.
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